Architecture
Physics-informed subsurface intelligence
We fuse legacy geoscientific datasets, satellite remote sensing, drill logs, and geophysical surveys into predictive subsurface models that pinpoint high-probability drill targets.
Data Layers
Multi-modal input processing
Four primary dataset layers fused directly into 3D deposit prospectivity maps.
Satellite
Hyperspectral Remote Sensing
Surface alteration mineralogy mapped via satellite telemetry with vector anomaly overlays.
Geophysics
Subsurface Geophysics
Gravity, magnetic, and radiometric surveys processed for deep-seated structural anomalies.
Drill Logs
Historical Core Data
Standardized lithological and geochemical assays ingested from legacy exploration archives.
Competitive Benchmark
Industry capabilities and market context
While industry benchmarks like KoBold Metals deploy data-driven exploration using proprietary capital, EarthScience.AI delivers transparent, physics-informed mineral system modeling that integrates seamlessly with existing technical evaluation workflows.
Our computational framework replaces opaque black-box predictions with verifiable geophysical evidence, ensuring exploration teams maintain absolute confidence in every generated drill target.
Methodology
From ingestion to verified targets
01
Dataset Ingestion
Harmonize multi-format geophysical, geochemical, and geological archives into a unified spatial database.
02
Feature Extraction
Isolate structural controls and alteration signatures using domain-specific spatial machine learning.
03
Prospectivity Modeling
Generate probabilistic 3D deposit models constrained directly by known mineral system physics.
04
Target Generation
Deliver ranked, auditable drill collars designed to minimize uncertainty in greenfield terranes.